English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 19 Hours | 6.74 GB

Learn to use Python for Deep Learning with Google’s latest Tensorflow 2 library and Keras!

This course will guide you through how to use Google’s latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow 2 framework in a way that is easy to understand.

We’ll focus on understanding the latest updates to TensorFlow and leveraging the Keras API (TensorFlow 2.0’s official API) to quickly and easily build models. In this course we will build models to forecast future price homes, classify medical images, predict future sales data, generate complete new text artificially and much more!

This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!

This course covers a variety of topics, including

- NumPy Crash Course
- Pandas Data Analysis Crash Course
- Data Visualization Crash Course
- Neural Network Basics
- TensorFlow Basics
- Keras Syntax Basics
- Artificial Neural Networks
- Densely Connected Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- AutoEncoders
- GANs – Generative Adversarial Networks
- Deploying TensorFlow into Production
- and much more!

Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains enhancements including eager execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines.

TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to publication. TensorFlow 2.0 incorporates a number of features that enables the definition and training of state of the art models without sacrificing speed or performance

It is used by major companies all over the world, including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel, and of course, Google!

Become a deep learning guru today! We’ll see you inside the course!

What you’ll learn

- Learn to use TensorFlow 2.0 for Deep Learning
- Leverage the Keras API to quickly build models that run on Tensorflow 2
- Perform Image Classification with Convolutional Neural Networks
- Use Deep Learning for medical imaging
- Forecast Time Series data with Recurrent Neural Networks
- Use Generative Adversarial Networks (GANs) to generate images
- Use deep learning for style transfer
- Generate text with RNNs and Natural Language Processing
- Serve Tensorflow Models through an API
- Use GPUs for accelerated deep learning

## Table of Contents

**Course Overview Installs and Setup**

1 Course Overview

2 Course Setup and Installation

3 FAQ – Frequently Asked Questions

**NumPy Crash Course**

4 Introduction to NumPy

5 NumPy Arrays

6 Numpy Index Selection

7 NumPy Operations

8 NumPy Exercises

9 Numpy Exercises – Solutions

**Pandas Crash Course**

10 Introduction to Pandas

11 Pandas Series

12 Pandas DataFrames – Part One

13 Pandas DataFrames – Part Two

14 Pandas Missing Data

15 GroupBy Operations

16 Pandas Operations

17 Data Input and Output

18 Pandas Exercises

19 Pandas Exercises – Solutions

**Visualization Crash Course**

20 Introduction to Python Visualization

21 Matplotlib Basics

22 Seaborn Basics

23 Data Visualization Exercises

24 Data Visualization Exercises – Solutions

**Machine Learning Concepts Overview**

25 What is Machine Learning

26 Supervised Learning Overview

27 Overfitting

28 Evaluating Performance – Classification Error Metrics

29 Evaluating Performance – Regression Error Metrics

30 Unsupervised Learning

**Basic Artificial Neural Networks – ANNs**

31 Introduction to ANN Section

32 Perceptron Model

33 Neural Networks

34 Activation Functions

35 Multi-Class Classification Considerations

36 Cost Functions and Gradient Descent

37 Backpropagation

38 TensorFlow vs. Keras Explained

39 Keras Syntax Basics – Part One – Preparing the Data

40 Keras Syntax Basics – Part Two – Creating and Training the Model

41 Keras Syntax Basics – Part Three – Model Evaluation

42 Keras Regression Code Along – Exploratory Data Analysis

43 Keras Regression Code Along – Exploratory Data Analysis – Continued

44 Keras Regression Code Along – Data Preprocessing and Creating a Model

45 Keras Regression Code Along – Model Evaluation and Predictions

46 Keras Classification Code Along – EDA and Preprocessing

47 Keras Classification – Dealing with Overfitting and Evaluation

48 TensorFlow 2.0 Keras Project Options Overview

49 TensorFlow 2.0 Keras Project Notebook Overview

50 Keras Project Solutions – Exploratory Data Analysis

51 Keras Project Solutions – Dealing with Missing Data

52 Keras Project Solutions – Dealing with Missing Data – Part Two

53 Keras Project Solutions – Categorical Data

54 Keras Project Solutions – Data PreProcessing

55 Keras Project Solutions – Creating and Training a Model

56 Keras Project Solutions – Model Evaluation

57 Tensorboard

**Convolutional Neural Networks – CNNs**

58 CNN Section Overview

59 Image Filters and Kernels

60 Convolutional Layers

61 Pooling Layers

62 MNIST Data Set Overview

63 CNN on MNIST – Part One – The Data

64 CNN on MNIST – Part Two – Creating and Training the Model

65 CNN on MNIST – Part Three – Model Evaluation

66 CNN on CIFAR-10 – Part One – The Data

67 CNN on CIFAR-10 – Part Two – Evaluating the Model

68 Downloading Data Set for Real Image Lectures

69 CNN on Real Image Files – Part One – Reading in the Data

70 CNN on Real Image Files – Part Two – Data Processing

71 CNN on Real Image Files – Part Three – Creating the Model

72 CNN on Real Image Files – Part Four – Evaluating the Model

73 CNN Exercise Overview

74 CNN Exercise Solutions

**Recurrent Neural Networks – RNNs**

75 RNN Section Overview

76 RNN Basic Theory

77 Vanishing Gradients

78 LSTMS and GRU

79 RNN Batches

80 RNN on a Sine Wave – The Data

81 RNN on a Sine Wave – Batch Generator

82 RNN on a Sine Wave – Creating the Model

83 RNN on a Sine Wave – LSTMs and Forecasting

84 RNN on a Time Series – Part One

85 RNN on a Time Series – Part Two

86 RNN Exercise

87 RNN Exercise – Solutions

88 Bonus – Multivariate Time Series – RNN and LSTMs

**Natural Language Processing**

89 Introduction to NLP Section

90 NLP – Part One – The Data

91 NLP – Part Two – Text Processing

92 NLP – Part Three – Creating Batches

93 NLP – Part Four – Creating the Model

94 NLP – Part Five – Training the Model

95 NLP – Part Six – Generating Text

**AutoEncoders**

96 Introduction to Autoencoders

97 Autoencoder Basics

98 Autoencoder for Dimensionality Reduction

99 Autoencoder for Images – Part One

100 Autoencoder for Images – Part Two – Noise Removal

101 Autoencoder Exercise Overview

102 Autoencoder Exercise – Solutions

**Generative Adversarial Networks**

103 GANs Overview

104 Creating a GAN – Part One- The Data

105 Creating a GAN – Part Two – The Model

106 Creating a GAN – Part Three – Model Training

107 DCGAN – Deep Convolutional Generative Adversarial Networks

**Deployment**

108 Introduction to Deployment

109 Creating the Model

110 Model Prediction Function

111 Running a Basic Flask Application

112 Flask Postman API

113 Flask API – Using Requests Programmatically

114 Flask Front End

115 Live Deployment to the Web

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